Design a Smart Helmet for Accident Detection and Location Tracking
Abstract
This study examines cutting-edge motorcycle safety systems that use crash detection and alert systems to lower accident mortality. These systems track motion dynamics and key angles to detect collisions using IMU sensors and tilt detection. Emergency alarms with accurate position data can be transmitted in real time thanks to integrated GPS and GSM devices. By preventing dangerous driving conditions, further features like alcohol detection and helmet usage enforcement improve rider safety. More precision and quicker reaction times are possible with future advancements like machine learning and real-time analytics. By increasing accident detection and emergency response effectiveness, these affordable solutions offer substantial advantages, especially in areas with extensive motorcycle usage the system consists of an ESP32 microprocessor and a multi-modal sensor package that includes MQ-3 alcohol, force-impact sensors, and an inertial measurement unit (IMU). Sensors to perform preventative safety checking. A time-windowed sensor-fusion algorithm was used to differentiate between legitimate collisions and typical riding dynamics in order to get beyond the single threshold constraint of unreliable systems. In order to minimize erroneous activations, this reasoning uses simultaneous cues of high-G inertial rotations and physical impacting features throughout a 500 ms time window. The system's architecture is entirely self-sufficient and uses an integrated GPS-GSM module to transmit the location via SMS without requiring a smartphone connection.
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